High spatio-temporal monitoring of century-old biochar effects on evapotranspiration through the ETLook model: a case study with UAV and satellite image fusion based on additive wavelet transform (AWT)

Abstract

ABSTRACT It can be challenging to fuse remotely-sensed images with large differences in spatial resolutions. In this paper, we used additive wavelet transform (AWT) to fuse Landsat-8 (30 m) and unmanned aerial vehicle (UAV) images (7 cm and 3.7 cm for thermal and multispectral images, respectively) as one of the primary studies. AWT image fusion generated sharpened Landsat-8 (L-8) images which were significantly correlated with coarse resolution images, while also well preserving the spatial details. Surface albedo (α0), normalized difference vegetation index (NDVI), and surface temperature (ST) were computed from multispectral and thermal sensors on board of UAV and L-8 platforms. High-resolution UAV and AWT sharpened L-8 images were then used in ETLook model to estimate evapotranspiration (ET) across an agricultural farm enriched with century-old biochar. High spatio-temporal analysis demonstrated a significant decrease in α0 across the biochar patches during the early development stages of winter wheat. Moreover, biochar significantly stimulated the development of wheat canopies towards the middle of the cropping season. There were however no impacts at the end of the season due to dense wheat canopies covering the aggravated dark colour soil across the biochar patches. ST was not affected by biochar either at the beginning or towards the end of the season. Neither was there any impact of biochar on actual ET over the season. Our approach can help to develop robust techniques for fusion of UAV and satellite images in light of climate-smart agriculture, and is also applicable to other farms with any specific precision agricultural treatments.